| 1. |
- Bengtsson, Kenneth, et al.
(author)
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Så kan Sverige bli ledande nation i resurseffektivitet
- 2016
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In: Dagens Nyheter. - 1101-2447. ; :2016-04-30
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Journal article (pop. science, debate, etc.)abstract
- Ny rapport. Det svenska näringslivet kan bli mer hållbart, resurssmart och därmed internationellt konkurrenskraftigt. Men för det behövs en tydlig politisk avsiktsförklaring och riktlinjer. Vi har listat sex områden där policyutveckling brådskar, skriver företrädare för näringsliv, forskning och myndigheter i en gemensam uppmaning.
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| 2. |
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| 3. |
- Brodén, Björn, et al.
(author)
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A Bandit-Based Ensemble Framework for Exploration/Exploitation of Diverse Recommendation Components : An Experimental Study within E-Commerce
- 2019
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In: ACM Transactions on Interactive Intelligent Systems. - : ACM Digital Library. - 2160-6455 .- 2160-6463. ; 10:1, s. 4:1-4:32
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Journal article (peer-reviewed)abstract
- This work presents an extension of Thompson Sampling bandit policy for orchestrating the collection of base recommendation algorithms for e-commerce. We focus on the problem of item-to-item recommendations, for which multiple behavioral and attribute-based predictors are provided to an ensemble learner. In addition, we detail the construction of a personalized predictor based on k-Nearest Neighbors (kNN), with temporal decay capabilities and event weighting. We show how to adapt Thompson Sampling to realistic situations when neither action availability nor reward stationarity is guaranteed. Furthermore, we investigate the effects of priming the sampler with pre-set parameters of reward probability distributions by utilizing the product catalog and/or event history, when such information is available. We report our experimental results based on the analysis of three real-world e-commerce datasets.
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| 4. |
- Brodén, Björn, et al.
(author)
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Bandit Algorithms for e-Commerce Recommender Systems Extended Abstract
- 2017
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In: Proceedings of the Eleventh ACM Conference On Recommender Systems (Recsys'17). - New York, NY, USA : ACM Digital Library. ; , s. 349-349
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Conference paper (peer-reviewed)abstract
- We study bandit algorithms for e-commerce recommender systems. The question we pose is whether it is necessary to consider reinforcement learning effects in recommender systems. A key reason to introduce a recommender system for a product page on an e-commerce site is to increase the order value by improving the chance of making an upsale. If the recommender system merely predicts the next purchase, there might be no positive effect at all on the order value, since the recommender system predicts sales that would have happened independent of the recommender system. What we really are looking for are the false negatives, i.e., purchases that happen as a consequence of the recommender system. These purchases entail the entire uplift and should be present as reinforcement learning effects. This effect cannot be displayed in a simulation of the site, since there are no reinforcement learning effects present in a simulation. The attribution model must capture the uplift to guarantee an increased order value. However, such an attribution model is not practical, due to data sparsity. Given this starting point, we study some standard attribution models for e-commerce recommender systems, and describe how these fare when applied in a reinforcement learning algorithm, both in a simulation and on live sites.
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| 5. |
- Brodén, Björn, et al.
(author)
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Ensemble Recommendations via Thompson Sampling : an Experimental Study within e-Commerce
- 2018
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In: Proceeding IUI '18 23rd International Conference on Intelligent User Interfaces. - New York, NY, USA : ACM Digital Library. ; , s. 19-29
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Conference paper (peer-reviewed)abstract
- This work presents an extension of Thompson Sampling bandit policy for orchestrating the collection of base recommendation algorithms for e-commerce. We focus on the problem of item-to-item recommendations, for which multiple behavioral and attribute-based predictors are provided to an ensemble learner. We show how to adapt Thompson Sampling to realistic situations when neither action availability nor reward stationarity is guaranteed. Furthermore, we investigate the effects of priming the sampler with pre-set parameters of reward probability distributions by utilizing the product catalog and/or event history, when such information is available. We report our experimental results based on the analysis of three real-world e-commerce datasets.
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| 6. |
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| 7. |
- Brodén, Björn, et al.
(author)
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Online and offline algorithms for the time-dependent TSP with time zones
- 2004
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In: Algorithmica. - : Springer Science and Business Media LLC. - 0178-4617 .- 1432-0541. ; 39:4, s. 299-319
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Journal article (peer-reviewed)abstract
- The time-dependent traveling salesman problem (TDTSP) is a variant of TSP with time-dependent edge costs. We study some restrictions of TDTSP where the number of edge cost changes are limited. We find competitive ratios for online versions of TDTSP. From these we derive polynomial time approximation algorithms for graphs with edge costs one and two. In addition, we present an approximation algorithm for the orienteering problem with edge costs one and two.
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